DETAILED ACTION
Applicant’s response filed 12/03/2025 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and are herein under examination.
Claims 1-20 are rejected.
Claims 1 and 11 are objected.
Priority
The instant application claims domestic benefit to U.S. Provisional Application No. 63/149,498 filed 02/15/2021. The claims to domestic benefit are acknowledged for claims 1-20. As such, the effective filing date for claims 1-20 is 02/15/2021.
Abstract
The objection to the abstract is withdraw in view of the amended abstract filed 12/03/2025.
Drawings
The objection to the drawings is withdrawn in view of the amended specification filed 12/03/2025.
Drawings filed 02/11/2022 are accepted.
Specification
The objection to the specification is withdrawn in view of the amended specification filed 12/03/2025.
Claim Objections
The objections to claims 1 and 11-13 are withdrawn in view of claim amendments.
Claims 1 and 11 are objected to because of the following informalities:
Claim 1, line 4, recites “automated” which should be “an automated”.
Claim 11, line 4, recites “perform automated” which should be “perform an automated”.
Appropriate correction is required.
Withdrawn Rejections
35 USC 112(b)
The rejection of claims 1-20 under 35 USC 112(b) is withdrawn in view of claim amendments.
Double Patenting
The provisional rejection on the ground of nonstatutory double patenting of claims 1-20 over copending Application No. 18/100,727 is withdrawn in view of claim amendments.
Claim Rejections - 35 USC § 112
35 USC 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is newly recited as necessitated by claim amendment.
Claim 1, lines 10-11, recites the phrase “the particular cell type in the admixture” which renders the claim indefinite. It is unclear which particular cell type is being referenced because each single-cell sample is associated with a particular cell type, as recited in lines 2-3. To overcome this rejection, clarify which cell type is being referenced.
Claim 1, lines 19, 28, 33, 43, 48, 53 and 59, recites the phrase “the at least one processor” which renders the claim indefinite. It is unclear which at least one processor is being referenced because line 4 recites “at least one processor” and line 7 recites “by at least one processor from at least one genotyping device”. To overcome this rejection, clarify which at least one processor is being referenced.
Claim 1, lines 21-22, recites the phrase “the magnitude of the measurement at each allele of each locus” which renders the claim indefinite. It is unclear which magnitudes of which alleles are being referenced because lines 15-16 recite “each allele corresponds to a magnitude of a measurement” and lines 20-21 recite “a plurality of magnitudes of at least one measurement at a plurality of alleles”. To overcome the rejection, clarify which magnitudes are being referenced.
Furthermore, claims 2-10 are also rejected because they depend on claim 1, which is rejected, and because they do not resolve the issue of indefiniteness.
Claims 2-4, 6, 8 and 10 recite the phrase “the at least one processor” which renders the claims indefinite. It is unclear which at least one processor is being referenced because claim 1, line 4, recites “at least one processor” and claim 1, line 7, recites “by at least one processor from at least one genotyping device”. To overcome this rejection, clarify which at least one processor is being referenced.
Claim 11, lines 2-3, recites the following phrase that renders the claim indefinite: “separating single-cell samples from a sample comprising an admixture of a plurality of cell types, each single-cell sample being associated with a particular cell type”. MPEP 2173.05(p) recites “A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.” See In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1318, 97 USPQ2d 1737, 1748-49 (Fed. Cir. 2011). In the instant case, the method step of “separating single-cell samples” in the system claim of 11 renders claim 11 indefinite.
For examination purposes, this phrase is being interpreted as a product by process. The phrase is a process previously performed, outside the metes and bounds of the claimed system, to obtain the single-cell samples used to create the sample set of signal profiles, as recited in lines 7-8. To overcome this rejection, remove the method step.
Claim 11, lines 9-10, recites the phrase “the particular cell type in the admixture” which renders the claim indefinite. It is unclear which particular cell type is being referenced because each single-cell sample is associated with a particular cell type, as recited in lines 2-3. To overcome this rejection, clarify which cell type is being referenced.
Claim 11, lines 19-20, recites the phrase “the magnitude of the measurement at each allele of each locus” which renders the claim indefinite. It is unclear which magnitudes of which alleles are being referenced because lines 14-15 recite “each allele corresponds to a magnitude of a measurement” and lines 18-19 recite “a plurality of magnitudes of at least one measurement at a plurality of alleles”. To overcome the rejection, clarify which magnitudes are being referenced.
Furthermore, claims 12-20 are also rejected because they depend on claim 11, which is rejected, and because they do not resolve the issue of indefiniteness.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 2A, Prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomena (Step 2A, Prong 1). In the instant application, claims 1-10 recite a method and claims 11-20 recite a system. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1 and 11 recite “performing … automated single-cell analysis deconvolution pipeline to produce forensically relevant analysis of a target contributor to the admixture, the automated single-cell analysis deconvolution pipeline comprising: for each cell of the plurality of cells: determining … a set of allele vectors representing a plurality magnitudes of at least one measurement at a plurality of alleles, comprising the magnitude of the measurement at each allele of each locus; and wherein each allele vector of the set of allele vectors is associated with each locus of the plurality of loci; and wherein the magnitude of the measurement at each allele is mapped to a predetermined index location in an associated allele vector of the set of allele vectors; generating … a cell vector in a set of cell vectors by concatenating each allele vector associated with each locus of the plurality of loci; wherein the set of cell vectors represent the sample set of signal profile; generating … a plurality of clustered data sets of the set of cell vectors by utilizing at least one cluster model to create at least one cluster of at least one subset of cell vectors of the set of cell vectors to group the signal profiles within the sample set of signal profiles; wherein utilizing the at least one cluster model comprises (i) computing pairwise similarity metrics between cell vectors to characterize profile similarity and (ii) performing model-based clustering to determine cluster assignments based on the pairwise similarity metrics; wherein each cluster is associated with a contributor of at least one contributor to the admixture; determining … a first likelihood of a presence of each subset of cell vectors of the at least one subset of cell vectors given that the target contributor of the at least one contributor supplied genetic material based at least in part on a comparison of a target signal profile of the signal profiles and each cluster; determining … a second likelihood of the presence of each subset of cell vectors of the at least one subset of cell vectors given that the target contributor of the at least one contributor did not supply genetic material based at least in part on a comparison of the target signal profile of the signal profiles and each cluster; determining … in response to the separating of the single-cell samples, a likelihood ratio based at least in part on a ratio of the first likelihood and the second likelihood to produce the forensically relevant analysis of the target contributor to the admixture, wherein the likelihood ratio is indicative of a probability of the target contributor having contributed cells to the admixture of the plurality of cell types; and generating … at least one visualization …, wherein the at least one visualization is based at least in part on the forensically relevant analysis of the target contributor.”
Claims 2 and 12 recite “determining … a likely number of contributors based at least in part on the at least one cluster; determining … that the likely number of contributors exceeds an amount of the at least one cluster; and generating … at least one additional cluster from the at least one cluster.”
Claims 3 and 13 recite “determining … a likely number of contributors based at least in part on the at least one cluster; wherein the at least one cluster is a plurality of clusters; determining … that an amount of the plurality of clusters exceeds the likely number of contributors; determining ... a subset of the plurality of clusters that are associated with a single contributor; and generating ... a single cluster from the subset of the plurality of clusters.”
Claims 4 and 14 recite “further comprising normalizing ... the set of cell vectors based at least in part on a log-normal distribution.”
Claims 5 and 15 recite “wherein the at least one cluster model comprises at least one mixture model.”
Claims 6 and 16 recite “further comprising utilizing ... the at least one mixture model to model the at least one cluster according to at least one probability distribution.”
Claims 7 and 17 recite “wherein the at least one probability distribution comprises at least one Gaussian distribution.”
Claims 8 and 18 recite “further comprising estimating ... parameters of the at least one cluster model based at least in part on an expectation-maximization algorithm.”
Claims 9 and 19 recite “wherein each allele vector of the set of allele vectors encodes: a true allele signal associated with a signal profile in the sample set of signal profiles, a noise associated with the signal profile in the sample set of signal profiles, and a reverse stutter associated with the signal profile in the sample set of signal profiles.”
Claims 10 and 20 recite “utilizing ... a Uniform Manifold Approximation and Projection model to generate a high dimensional graph representation of the at least one cluster of the at least one subset of cell vectors; and generating ... at least one visualization comprising the high dimensional graph representation.”
Limitations reciting a mental process.
The above cited limitations in claims 1-8, 10-18 and 20 are recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind or by a human using pen and paper. The paragraphs below discuss the broadest reasonable interpretation (BRI) of the limitations in these claims that recite a mental process:
Regarding the above cited limitations in claims 1 and 11, the BRI of performing an automated single-cell analysis deconvolution pipeline to produce analysis includes performing the following abstract ideas. The BRI of determining a set of cell vectors includes a human acquiring data points and generating a one-dimensional array of numbers, such as shown in Figure 13. The BRI of mapping a magnitude to a predetermined index includes comparing data. The BRI of concatenating cell vectors includes adding the values of cell vectors together. The BRI of using a cluster model to group signal profiles using cell vectors includes inputting vectors into a k-means clustering algorithm, wherein a human could practically perform such calculations. The BRI of determining a first and second likelihood includes performing calculations such as those described in specification paras. [135-137]. The BRI of calculating a likelihood ratio using the first and second likelihood includes performing a calculation such as that described in specification para. [89]. The BRI of generating a visualization that displays a likelihood ratio includes writing down the likelihood ratio on a piece of paper, which a human could practically do.
Regarding the above cited limitations in claims 2-3 and 12-13, a human can look at a number of clusters and determine a likely number of contributors (i.e., one cluster per contributor). A human could also determine if a number exceeds a likelihood as well as perform an additional clustering iteration using the k-means algorithm. A human could also use the clusters to generate a subset of clusters by performing an additional iteration of the k-means algorithm.
Regarding the above cited limitations in claims 4 and 14, a human could apply a logarithm transformation to data using a log-normal distribution to normalize cell vectors using the calculations described in specification para. [106].
Regarding the above cited limitations in claims 5-8 and 15-18, a human could practically use a mixture model because it includes performing the calculations of a multivariate Gaussian mixture model, which contains multivariate normal distributions, wherein the parameters of the model are estimated by an expectation-maximization algorithm.
Regarding the above cited limitations in claims 10 and 20, a human could practically perform the operations of a Uniform Manifold Approximation and Projection to then graph the output.
Limitations reciting a mathematical concept.
The above cited limitations in claims 1-8, 10-18 and 20 equate to a mathematical concept because these limitations are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The paragraphs below discuss the broadest reasonable interpretation (BRI) of the limitations in these claims that recite a mathematical concept:
Regarding the above cited limitations in claims 1 and 11, the BRI of a cluster model includes using a function such as those described in specification para. [86] as well as performing calculations such as computing pairwise similarity metrics. The BRI of determining a first and second likelihood to then calculate a likelihood ratio includes performing calculations to derive the likelihoods and ratio. Specification para. [89] shows an equation that can be used to calculate the likelihood ratio. Specification paras. [135-136] show example equations for the first and second likelihood.
Regarding claims 2-3 and 12-13, the BRI of generating clusters using a cluster model includes performing calculations using a mathematical function such as those described in specification para. [89] as well as using a k-means clustering algorithm.
Regarding claims 4 and 14, the BRI of normalizing data with a log-normal distribution includes performing a mathematical transformation on data, specifically described in specification para. [106].
Regarding claims 5-8 and 15-18, the BRI of using a mixture model, a probability distribution, a Gaussian distribution, and an expectation-maximization algorithm include performing calculations using functions.
Regarding claims 10 and 20, a Uniform Manifold Approximation Projection is a dimensionality reduction function that requires calculations.
Limitations reciting a natural phenomenon.
The above cited limitations in claims 1 and 11 of determining a contributor to a sample set of signal profiles based on a contributor’s alleles equates to a natural phenomenon because these limitations are similar to the concept of a correlation between variations in non-coding regions of DNA and allele presence in coding regions of DNA, Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1375, 118 USPQ2d 1541, 1545 (Fed. Cir. 2016), which the courts have established as a natural phenomenon. Specifically, identifying an individual based upon their genetic makeup is a natural phenomenon.
Limitations included in the recited judicial exception.
The above cited limitations in claims 9 and 19 are included in the judicial exception in claims 1 and 11, respectively, because they further limit the set of allele vectors but do not change the fact that the allele vectors are part of the judicial exception.
As such, claims 1-20 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: Yes).
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)), insignificant extra-solution activity (MPEP § 2106.05(g)), and field of use limitations (MPEP § 2106.05(h)). The instant claims recite the following additional elements:
Claim 1 recites “separating single-cell samples from an admixture of a plurality of cell types, each single-cell sample being associated with a particular cell type; … by at least one processor …; obtaining, by at least one processor from at least one genotyping device, a sample set of signal profiles comprising genotyping readouts obtained from the single-cell samples; wherein the signal profiles are associated with a plurality of cells of the particular cell type in the admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; and wherein each allele corresponds to a magnitude of a measurement of at least one genotyping readout of the genotyping readouts; .. by the at least one processor …; … on at least one computing device associated with at least one user.”
Claims 1-4, 6, 8 and 10 recite “by the at least one processor”
Claim 11 recites “A system comprising: separating single-cell samples from an admixture of a plurality of cell types, each single-cell sample being associated with a particular cell type; at least one processor configured to perform … obtain, from at least one genotyping device, a sample set of signal profiles comprising genotyping readouts obtained from the single-cell samples; wherein the signal profiles are associated with a plurality of cells of the particular cell type in the admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; and wherein each allele corresponds to a magnitude of a measurement of at least one genotyping readout of the genotyping readouts; … on at least one computing device associated with at least one user.”
Claims 12-14, 16, 18 and 20 recite “wherein the at least one processor is further configured to perform steps to”
Claims 12-20 recites “The system of claim 11/15/16”
Regarding the above cited limitations in claims 1-20 of “by a/the at least one processor”, “by at least one processor from at least one genotyping device”, “a system comprising at least one processor configured to perform steps to”, “wherein the at least one processor is further configured to perform steps to”, and “on at least one computing device”. These limitations do not require anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitations in claims 1 and 11 of separating single-cell samples from a sample comprising an admixture and obtaining a sample set of signal profiles from a genotyping device, these limitations equate to insignificant, extra-solution activity of necessary data gathering because they acquire data necessary to perform the judicial exception in claims 1 and 11.
As such, claims 1-20 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: No).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite the following additional elements:
Claim 1 recites “separating single-cell samples from an admixture of a plurality of cell types, each single-cell sample being associated with a particular cell type; … by at least one processor …; obtaining, by at least one processor from at least one genotyping device, a sample set of signal profiles comprising genotyping readouts obtained from the single-cell samples; wherein the signal profiles are associated with a plurality of cells of the particular cell type in the admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; and wherein each allele corresponds to a magnitude of a measurement of at least one genotyping readout of the genotyping readouts; .. by the at least one processor …; … on at least one computing device associated with at least one user.”
Claims 1-4, 6, 8 and 10 recite “by the at least one processor”
Claim 11 recites “A system comprising: separating single-cell samples from an admixture of a plurality of cell types, each single-cell sample being associated with a particular cell type; at least one processor configured to perform … obtain, from at least one genotyping device, a sample set of signal profiles comprising genotyping readouts obtained from the single-cell samples; wherein the signal profiles are associated with a plurality of cells of the particular cell type in the admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; and wherein each allele corresponds to a magnitude of a measurement of at least one genotyping readout of the genotyping readouts; … on at least one computing device associated with at least one user.”
Claims 12-14, 16, 18 and 20 recite “wherein the at least one processor is further configured to perform steps to”
Claims 12-20 recites “The system of claim 11/15/16”
Regarding the above cited limitations in claims 1-20 of “by a/the at least one processor”, “by at least one processor from at least one genotyping device”, “a system comprising at least one processor configured to perform steps to”, “wherein the at least one processor is further configured to perform steps to”, and “on at least one computing device”. These limitations do not require anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Regarding the above cited limitations in claims 1 and 11 of obtaining a sample set of signal profiles from a genotyping device, these limitations equate to receiving/transmitting data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Regarding the above cited limitations in claims 1 and 11 of “wherein the signal profiles are associated with a plurality of cells of an admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; wherein each allele comprises a magnitude of a measurements”, these limitations also equate to transmitting/receiving data over a network because they limit the type of data but do not change the fact that data is being transmitted/received.
Regarding the above cited limitations in claims 1 and 11 of separating single-cell samples from a sample comprising an admixture, this limitation is WURC as taught by the instant specification and by Fontana et al. (“Fontana”; Forensic Science International: Genetics 29 (2017): 225-241; newly cited). Specification para. [52] discusses the conventionality of performing single-cell isolation and DNA extraction from single-cells using commercially available kits. Fontana discloses isolation and genetic analysis of pure cells from forensic biological admixtures (title). Fontana uses an admixture containing cells of two or more individuals to create single genetic profiles obtained by separating distinct cell types performed by using DEPArray (abstract). Single cell isolation and genotyping on isolated single cells is performed in combination with computer software such as GeneMapper (pg. 228, col. 2, para. 1-2), indicating that the method is WURC when viewed in combination with a generic computer.
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to WURC functions/components of a generic computer and to WURC limitations for isolating single-cells of an admixture, as taught by Fontana and the specification. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-20 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 12/03/2025 have been fully considered but they are not persuasive.
Applicant argues that the claims do not recite mental process or mathematical concept (pg. 12, sec. a). These remarks are acknowledged but are not persuasive because no analysis of the claims has been presented.
Applicant argues that claim 1 contains a practical application in the field of forensic genetic analysis and lists limitations believed to be additional elements that confer the alleged practical application (pg. 12, sec. b – pg. 16, para. 1). Applicant’s argument is not persuasive for the following reasons:
From the list of alleged additional elements, the additional elements identified in the above 101 analysis include: separating single-cell samples, by at least one processor, obtaining a sample set of signal profiles, and at least one computing device associated with at least one user. These additional elements equate to mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)) and to mere data gathering activity (MPEP 2106.05(g)(3)), both of which do not integrate a judicial exception into a practical application.
Moreover, it appears that the alleged improvement is a result of performing the single-cell analysis deconvolution pipeline. However, the steps in the pipeline have been identified as reciting abstract ideas. MPEP 2105.05(a) recites that “the judicial exception alone cannot provide the improvement.”
Applicant argues the claims recite a specific architecture and operations that improve a computer’s efficiency to analyze genetic profiles to produce forensically relevant results. Applicant compares instant claims to Enfish (pg. 16, para. 2-3 of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
Applicant does not specify what the particular architecture(s) or operation(s) in the claims is/are.
Furthermore, instant claims 1 and 11 are different from Enfish because the self-referential table in Enfish improved computer functionality by improving the way a computer stores and retrieves data in memory. There is no evidence to suggest that the instant claims improve computer functionality in the same way as Enfish. Rather, instant claims 1 and 11 provide a better abstract idea (i.e. the pipeline) that inherently requires less computing power. Nothing about the computer itself is improved. For example, neither the way a processor functions nor the way in which the computer stores or accesses memory is improved. Nothing about the physical components of the computer nor the way the computer operates is changed merely by providing the computer with a better abstract idea. Therefore, the instant claims merely invoke computers as a tool.
Applicant’s reference to Enfish in Desjardins is considered but is not persuasive because in Enfish the software limitation was identified as reciting an additional element (pg. 17, para. 1 of Applicant’s remarks). The software/algorithms in the instant claims have been identified as reciting abstract ideas.
Applicant argues that the claims use machine learning innovations to improve computer efficiency and improve model-based clustering and single-cell pipeline by use of a particular application of feature engineering and machine learning technologies (pg. 17, last two para. of Applicant’s remarks). Applicant’s arguments are not persuasive for the following reasons:
It’s unclear which innovations are being referenced in the claims. It’s also unclear what feature engineering and machine learning technologies are particularized to improve model-based clustering and single-cell pipelines. As discussed in the response above, the pipeline in claims 1 and 11 recite abstract ideas. The abstract idea itself cannot provide the alleged improvement (MPEP 2106.05(a)).
Moreover, Desjardins was directed to improvements in how machine learning models are trained. However, Applicant has not provided argument or evidence for how model-based clustering has been improved. It’s also unclear if Applicant asserts that model-based clustering itself is improved, or if they are merely providing better/new data to existing model-based clustering methods.
Applicant argues that claim 1 recites additional elements that are not WURC (pg. 18, sec. c of Applicant’s remarks). Applicant’s argument is not persuasive for the reasons described in the rejection above under section Step 2B.
The arguments applied above regarding claim 1 apply to claim 11.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/100,727 (hereinafter “App. ‘727”) in view of Fontana et al. (“Fontana”; Forensic Science International: Genetics 29 (2017): 225-241; newly cited) and Zhong et al. (“Zhong”; Journal of machine learning research 4, no. Nov (2003): 1001-1037; newly cited).
This rejection is newly recited as necessitated by claim amendment.
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are an obvious variation of the claims in App. ‘727. The following table shows claims in App. ‘727 that read on the claims of the instant application:
Instant Application claims
App. ‘727 claims
1: A method comprising: performing, by at least one processor, automated single-cell analysis deconvolution pipeline to produce forensically relevant analysis of a target contributor to the admixture, the automated single-cell analysis deconvolution pipeline comprising: obtaining, by at least one processor, a sample set of signal profiles; wherein the signal profiles are associated with a plurality of cells of an admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; wherein each allele comprises a magnitude of a measurements; for each cell of the plurality of cells: determining ... a set of cell vectors representing the magnitude of the measurement at each allele of each locus; wherein each vector of the set of cell vectors is associated with each locus of the plurality of loci; wherein the magnitude of the measurement at each allele is mapped to a predetermined index location in an associated vector of the set of cell vectors; generating ... a cell vector in a set of cell vectors by concatenating each vector associated with each locus of the plurality of loci; wherein the set of cell vectors represent the sample set of signal profiles; utilizing ... at least one cluster model to create at least one cluster of at least one subset of cell vectors of the set of cell vectors in order to group the signal profiles within the sample set of signal profiles; wherein each cluster is associated with a contributor of at least one contributor; determining ... a first likelihood of each subset of cell vectors of the at least one subset of cell vectors given that a target contributor of the at least one contributor supplied genetic material based at least in part on a comparison of a target signal profile and each cluster; determining ... a second likelihood of each subset of cell vectors of the at least one subset of cell vectors given that the target contributor of the at least one contributor did not supply genetic material based at least in part on a comparison of the target signal profile and each cluster; determining, by the at least one processor, in response to the separating of the single-cell samples, a likelihood ratio based at least in part on a ratio of the first likelihood and the second likelihood to produce the forensically relevant analysis of the target contributor to the admixture, wherein the likelihood ratio is indicative of a probability of the target contributor having contributed cells to the admixture of the plurality of cell types; and generating, by the at least one processor, at least one visualization on at least one computing device associated with at least one user, wherein the at least one visualization based at least in part on the forensically relevant analysis of the target contributor.
1: A method comprising: receiving, by at least one processor, a sample set of signal profiles; wherein the sample set of signal profiles are associated with a plurality of cells of an admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; wherein each allele comprises a magnitude of a measurement; for each cell of the plurality of cells: determining ... a set of cell vectors representing the magnitude of the measurement at each allele of each locus; wherein each vector of the set of cell vectors is associated with each locus of the plurality of loci; wherein the magnitude of the measurement at each allele is mapped to a predetermined index location in an associated vector of the set of cell vectors; generating ... a cell vector in a set of cell vectors by concatenating each vector associated with each locus of the plurality of loci; wherein the set of cell vectors represent the sample set of signal profiles; utilizing ... at least one cluster model to create a plurality of clusters for a plurality of subsets of cell vectors of the set of cell vectors in order to group signal profiles within the sample set of signal profiles; wherein each cluster is associated with an unknown contributor of a plurality of contributors; determining ... a first probability of each subset of cell vectors of the plurality of subsets of cell vectors given that a target contributor of the plurality of contributors supplied genetic material based at least in part on a comparison of a target signal profile and each cluster; determining ... a second probability of each subset of cell vectors of the plurality of subsets of cell vectors given that the target contributor of the plurality of contributors did not supply genetic material based at least in part on a comparison of the target signal profile and each cluster; determining ... a likelihood ratio for each cluster based at least in part on a ratio of the first probability and the second probability; and generating ... at least one visualization on at least one computing device associated with at least one user, wherein the at least one visualization displays the average likelihood ratio.
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11: A system comprising: at least one processor configured to perform steps to: automated single-cell analysis deconvolution pipeline to produce forensically relevant analysis of a target contributor to the admixture, the automated single-cell analysis deconvolution pipeline comprising steps to: obtain a sample set of signal profiles; wherein the signal profiles are associated with a plurality of cells of an admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; wherein each allele comprises a magnitude of a measurements; for each cell of the plurality of cells: determine a set of cell vectors representing the magnitude of the measurement at each allele of each locus; wherein each vector of the set of cell vectors is associated with each locus of the plurality of loci; wherein the magnitude of the measurement at each allele is mapped to a predetermined index location in an associated vector of the set of cell vectors; generate a cell vector in a set of cell vectors by concatenating each vector associated with each locus of the plurality of loci; wherein the set of cell vectors represent the sample set of signal profiles; utilize at least one cluster model to create at least one cluster of at least one subset of cell vectors of the set of cell vectors in order to group the signal profiles within the sample set of signal profiles; wherein each cluster is associated with a contributor of at least one contributor; determine a first likelihood of each subset of cell vectors of the at least one subset of cell vectors given that a target contributor of the at least one contributor supplied genetic material based at least in part on a comparison of a target signal profile and each cluster; determine a second likelihood of each subset of cell vectors of the at least one subset of cell vectors given that the target contributor of the at least one contributor did not supply genetic material based at least in part on a comparison of the target signal profile and each cluster; determine, in response to the separating of the single-cell samples, a likelihood ratio based at least in part on a ratio of the first likelihood and the second likelihood to produce the forensically relevant analysis of the target contributor to the admixture, wherein the likelihood ratio is indicative of a probability of the target contributor having contributed cells to the admixture of the plurality of cell types; and generate at least one visualization on at least one computing device associated with at least one user, wherein the at least one visualization is based at least in part on the forensically relevant analysis of the target contributor.
11: A system comprising: at least one processor configured to perform steps to: receive a sample set of signal profiles; wherein the signal profiles are associated with a plurality of cells of an admixture; wherein each cell of the plurality of cells comprises a plurality of loci; wherein each locus of the plurality of loci comprises a plurality of alleles; wherein each allele comprises a magnitude of a measurement; for each cell of the plurality of cells: determine a set of cell vectors representing the magnitude of the measurement at each allele of each locus; wherein each vector of the set of cell vectors is associated with each locus of the plurality of loci; wherein the magnitude of the measurement at each allele is mapped to a predetermined index location in an associated vector of the set of cell vectors; generate a cell vector in a set of cell vectors by concatenating each vector associated with each locus of the plurality of loci; wherein the set of cell vectors represent the sample set of signal profiles; utilize at least one cluster model to create a plurality of clusters of for a plurality of subsets of cell vectors of the set of cell vectors in order to group the signal profiles within the sample set of signal profiles; wherein each cluster is associated with an unknown contributor of a plurality of contributors; determine a first probability of each subset of cell vectors of the plurality of subsets of cell vectors given that a target contributor of the plurality of contributors supplied genetic material based at least in part on a comparison of a target signal profile and each cluster; determine a second probability of each subset of cell vectors of the plurality of subsets of cell vectors given that the target contributor of plurality of contributors did not supply genetic material based at least in part on a comparison of the target signal profile and each cluster; determine a likelihood ratio for each cluster based at least in part on a ratio of the first probability and the second probability; and generate at least one visualization on at least one computing device associated with at least one user, wherein the at least one visualization displays the average likelihood ratio.
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App. ‘727 does not teach the following limitations in claims 1 and 11: “separating single-cell samples from an admixture of a plurality of cell types, each single-cell sample being associated with a particular cell type; by at least one processor from at least one genotyping device; comprising genotyping readouts obtained from the single-cell samples; of at least one genotyping readout of the genotyping readouts”.
Fontana uses an admixture containing cells of two or more individuals to create single genetic profiles obtained by separating distinct cell types performed by using DEPArray (abstract). The isolated cells undergo genotyping using AmpFLSTR NGM SElect PCR Amplification Kit and allele calls were made using GeneMapper software (sec. 2.1.5).
It would have been prima facie obvious to modify App. ‘727 by isolating individual cells from an admixture and performing a genotype on isolated cells, as taught by Fontana, because App. ‘727 uses previously obtained genotypes from previously isolated single cells from an admixture. One of ordinary skill in the art would have had a reasonable expectation of success because Fontana demonstrates that single cells from an admixture can be isolated and genotyped.
App. ‘727 and Fontana do not teach the following limitations in claims 1 and 11: “(i) computing pairwise similarity metrics between cell vectors to characterize profile similarity and (ii) performing model-based clustering to determine cluster assignments based on the pairwise similarity metrics.”
Zhong teaches a unified framework for model-based clustering (title). Zhong discloses a hierarchical agglomerative clustering algorithm (HAC) that computes pairwise inter-cluster distance using an appropriate similarity measure (Figure 5). Zhong states that the pairwise distances have to be calculated and form the basis for computing-inter cluster distance (pg. 1010, para. 1).
It would have been prima facie obvious to have modified the cluster model in App. ‘727 by using the model-based HAC, as taught by Zhong, to compute pairwise distances using a similarity measure between cell vectors in order to perform clustering. The motivation or doing so is that App. ‘727 requires use of a model-based clustering method, wherein Zhong provides a model-based HAC that has advantages of providing a series of nested clustering for interactive analysis (pg. 1009, sec. 2.3, para. 1). One of ordinary skill in the art would have had a reasonable expectation of success because the model-based HAC of Zhong is a model-based clustering method.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Response to Arguments Double Patenting
Applicant’s arguments filed 12/03/2025 have been considered but are not persuasive because no terminal disclaimer has been received (pg. 18, last para. of Applicant’s remarks).
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685